Applied Math · Mathematics & Statistics
Numerical Analysis
The newest papers in Numerical Analysis that ReadSearch has read, newest first. Each links to its own page, with its authors, date, where it was published and the original.
285 papers, from 31 August 2026 to 5 November 2026: 212 arXiv preprints and 73 OpenAlex records.
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Newest papers, page 6
Block encoding of semiseparable matrices via their generators
We present an explicit quantum circuit that constructs a block encoding of a generator-representable semiseparable (one-pair) matrix directly from its two generators.Sustainable Cloud-Fog-Edge Systems for Smart Buildings: Algorithmic Adaptation under Memory Constraints and Dynamic Orchestration Strategies
The escalating environmental footprint of the Information and Communication Technol- ogy (ICT) sectorwith global Data Center electric- ity usage potentially reaching 3% by 2030 and the imperative…Constrained Probabilistic Numerics: Gaussian Process Solvers for Linear PDEs under Linear Inequality Constraints
Gaussian process solvers for linear partial differential equations (PDEs) return posteriors rather than point estimates, but they condition only on equality information: the PDE residual, boundary…A splitting proximal method in portfolio optimization with entropic value-at-risk
Motivated by a recent paper where a convex portfolio optimization problem formulated by means of the entropic value-at-risk is solved via a primal-dual interior-point method, we propose an…A structure-preserving neural density functional for the ions of a polymer electrolyte
Predicting the structure and response of inhomogeneous polymer electrolytes requires a description of ion correlations that retains molecular-scale accuracy while remaining transferable across…asdex: Automatic Sparse Differentiation in JAX
Many tasks in scientific computing and machine learning require the Jacobian or Hessian matrix of a function.Mesh-Native Tokenisation: Retrofitting Foundation Models to Unstructured Data
Physics foundation models are an increasingly popular approach for building versatile emulators for spatiotemporal systems.Polyconvex neural-network surrogates with learned paired directional features for anisotropic hyperelastic homogenization
Approximating the effective anisotropic hyperelastic response of a representative volume element (RVE) requires constitutive expressiveness, mechanical consistency, and computational efficiency.Improved zero-free half-planes for the quasi-Riemann hypothesis
Recently, a uniform $\frac{7}{8}$ zero-free half-plane for finite-order Hecke $L$-functions over $\mathbb Q(\sqrt{-3})$ and its transfer to Dirichlet $L$-functions was given by OpenAI in [Ope26a].Homogenization of weakly reinforced Pólya urns on countable networks
We consider weakly reinforced Pólya urns on a countable graph of uniformly bounded degree.The rank of 3x3 matrix multiplication over F2 is 23
The rank of the tensor of 3×3 matrix multiplication over the field with two elements is at most 23 by Laderman's algorithm, and Rudich and Rousseau recently proved that it is at least 22.Particle-Number-Preserving Quantum Signal Processing for Thermal Properties: Canonical-Ensemble Estimation and Grand-Canonical Reconstruction
We develop an end-to-end framework for estimating canonical thermal properties of particle-number-conserving second-quantized Hamiltonians using quantum simulations.An extended deep energy method for thermo-mechanical crack propagation
Thermo-mechanical fracture couples transient heat conduction on a cracked domain with a crack that grows as the temperature and the displacement evolve.Physics-Informed Neural Plasticity: PDE Solvers That Reshape Themselves
Physics-informed neural PDE solvers adapt their parameters to satisfy governing equations, yet their representational structure typically remains fixed throughout training.CAFE+FNO: Fourier Kernel Generation via Multiplicative Feature Composition
The Fourier Neural Operator (FNO) learns solution operators of partial differential equations (PDEs) through Fourier-space kernel parameterization, but frequency truncation can limit the learning of…Neural Sampling with Reweighted Normalizing Flows via the Wasserstein--Fisher--Rao JKO Scheme
We propose a neural algorithm for sampling from distributions specified by unnormalized Boltzmann densities.Plunge bounds for Fourier concentration operators: geometry, smoothing, and wave packets
We prove a single-logarithm bound for the eigenvalues in the plunge region of Fourier concentration operators, removing the extra logarithmic factor in the Kulikov--Dam Larsen bound for two general…A Neural JKO Scheme for Hellinger-Kantorovich Gradient Flows via Monge-Growth Pairs
We develop a mesh-free neural JKO scheme for advection-reaction-diffusion equations with a gradient-flow structure in the Hellinger-Kantorovich (HK) geometry of unbalanced optimal transport.Learned Adaptive Multiresolution Diffusion Imaging
Adaptive multiresolution methods reduce representation cost by concentrating fine-scale degrees of freedom where needed, but their tree updates are usually governed by fixed local criteria.FOSLS-deRhaNN: native de Rham neural classes for H(div) and H(curl) with applications to first-order system least-squares neural network methods for partial differential equations
We construct neural approximation classes native to the graph spaces H(div) and H(curl), in two and three dimensions and, for H(div), in any dimension.